The issue of left before treatment complete (LBTC) patients is common in emergency departments (EDs). This issue represents a medico-legal risk and may cause a revenue loss. Thus, understanding the factors that cause patients to leave before treatment is complete is vital to mitigate and potentially eliminate these adverse effects. This paper proposes a framework for studying the factors that affect LBTC outcomes in EDs. The framework integrates machine learning, metaheuristic optimization, and model interpretation techniques. Metaheuristic optimization is used for hyperparameter optimization--one of the main challenges of machine learning model development. Three metaheuristic optimization algorithms are employed for optimizing the parameters of extreme gradient boosting (XGB), which are simulated annealing (SA), adaptive simulated annealing (ASA), and adaptive tabu simulated annealing (ATSA). The optimized XGB models are used to predict the LBTC outcomes for the patients under treatment in ED. The designed algorithms are trained and tested using four data groups resulting from the feature selection phase. The model with the best predictive performance is interpreted using SHaply Additive exPlanations (SHAP) method. The findings show that ATSA-XGB outperformed other mode configurations with an accuracy, area under the curve (AUC), sensitivity, specificity, and F1-score of 86.61%, 87.50%, 85.71%, 87.51%, and 86.60%, respectively. The degree and the direction of effects of each feature were determined and explained using the SHAP method.

In this paper, we introduce a novel optimization algorithm for machine learning model training called Normalized Stochastic Gradient Descent (NSGD) inspired by Normalized Least Mean Squares (NLMS) from adaptive filtering. When we train a high-complexity model on a large dataset, the learning rate is significantly important as a poor choice of optimizer parameters can lead to divergence. The algorithm updates the new set of network weights using the stochastic gradient but with $\ell_1$ and $\ell_2$-based normalizations on the learning rate parameter similar to the NLMS algorithm. Our main difference from the existing normalization methods is that we do not include the error term in the normalization process. We normalize the update term using the input vector to the neuron. Our experiments present that the model can be trained to a better accuracy level on different initial settings using our optimization algorithm. In this paper, we demonstrate the efficiency of our training algorithm using ResNet-20 and a toy neural network on different benchmark datasets with different initializations. The NSGD improves the accuracy of the ResNet-20 from 91.96\% to 92.20\% on the CIFAR-10 dataset.

蜂窝网络（LTE，5G及以后）的增长急剧增长，消费者的需求很高，并且比具有先进的电信技术的其他无线网络更有希望。这些网络的主要目标是将数十亿个设备，系统和用户连接到高速数据传输，高电池容量和低延迟，以及支持广泛的新应用程序，例如虚拟现实，元评估，远程医疗，在线教育，自动驾驶汽车，高级制造等。为了实现这些目标，使用人工智能（AI）方法来实现频谱管理的新方法，以实现这些目标。本文使用基于AI的语义分割模型对光谱传感方法进行了脆弱性分析，以在具有防御性蒸馏方法的情况下识别对抗性攻击下的蜂窝网络信号。结果表明，缓解方法可以显着减少针对对抗攻击的基于AI的光谱传感模型的漏洞。

在这项研究中，我们旨在提供出于语言动机的解决方案，以解决缺乏无效词素的代表性，高生产力的衍生过程和土耳其语中的融合词素的问题，而在Boun Treebank中没有与普遍的依赖关系框架不同。为了解决这些问题，通过将某些引理并在UD框架中使用MISC（其他）选项卡来表示新的注释约定来表示派生。在基于LSTM的依赖性解析器上测试了重新注释的树库的代表性功能，并引入了船工具的更新版本。

由于它们在自然语言处理工具的开发中所扮演的关键作用，因此优质树仓的价值正在稳步增长。这种树仓的创造是劳动密集型且耗时的。尤其是当考虑树库的大小时，支持注释过程的工具至关重要。但是，已经提出了各种注释工具，但是它们通常不适合土耳其语等凝集性语言。 V1是用于注释依赖关系的船，随后被用于创建手动注释的Boun Treebank（UD_TURKISH-BOUN）。在这项工作中，我们根据使用船V1获得的经验报告了依赖性注释工具船V2的设计和实施，这揭示了一些改进的机会。 V2是一种多用户和基于Web的依赖性注释工具，设计为注释用户体验以产生有效的注释。该工具的主要目标是：（1）支持以提高速度创建有效且一致的注释，（2）显着改善注释者的用户体验，（3）支持注释者之间的协作，（4）提供开放 - 通过灵活的应用程序编程接口（API）来源和易于部署的基于Web的注释工具，以使科学界受益。本文讨论了船V2的启发，设计和实施以及示例。